Issue link: https://read.dmtmag.com/i/84685
The lower portion of Figure 2 identifies the underlying organizational structure of georegistered map data. An "analysis frame" delineates the geographic extent of the area of interest and, in the case of raster data, the size of each pixel/grid element. In the example, the image pixel size for the visual backdrop is less than a foot, comprising more than 4 million values, and the grid-cell size for analysis is 30 meters stored as a matrix with 99 columns and 99 rows, totaling nearly 10,000 individual cell locations. For georeferencing, the lower-left grid cell is identified as the matrix's origin (column 1, row 1) and is stored in decimal degrees of latitude and longitude along with other configuration parameters as a few header lines in the file containing the matrix of num- bers. In most instances, the huge matrix of numbers is compressed to minimize storage, but uncompressed on-the-fly for display and analytical processing. Inherently Uncomplicated? Figure 3 illustrates a broader level of organization for grid-based data. Within this construct, each grid map layer in a geographically registered analysis frame forms a separate theme, such as roads, cover type, image and elevation. Each point, line and polygon map feature is identified as a grid-cell grouping having a unique value stored in an implied matrix characterizing a discrete spatial variable. A surface gradient, however, is composed of fluctuating values that track the uninterrupted increases/decreases of a continuous spatial variable. The entire set of grid layers available in a database is termed a map stack. In map analysis, the appropriate grid layers are retrieved, their vales "map-ematically" processed and the resulting matrix stored in the stack as a new layer—in the same manner as solving an algebraic equation, except that the variables are entire grid maps composed of thousands of geographically organized numbers. The major advantages of grid-based maps are their inherently uncomplicated data structure and consistent parsing within a holistic characterization of geographic space—just the way computers and math/stat mindsets like it. There are no sets of irregular spatial objects scattered about an area that are assumed to be completely uniform within their interiors. Rather, they have continuously defined spatial features and gradients that better align with geographic reality and, for the most part, the traditional math/stat legacy. Next month's discussion builds on this point by extending grid maps and map analysis to "a universal key" for unlocking spatial relationships and patterns within standard database and quantitative-analysis approaches and procedures. Figure 3. A set of georegistered map layers forms a "map stack" organized as thousands of numbers within a common "analysis frame." SEPTEMBER 2O12 / WWW . GEOPLA CE .C O M 11 Figure 2. A raster grid contains map values for each "grid cell" identifying the characteristic/ condition at that location, supporting quantitative analysis. Author's Note: For a more detailed discussion of vector and raster data types and important considerations, see Topic 18, "Understanding Grid-based Data," in the online book, Beyond Mapping III, at www.innovativegis.com/basis/MapAnalysis.